Getting Started with Multilevel Modeling in R by Jared E. Knowles.
From the post:
Analysts dealing with grouped data and complex hierarchical structures in their data ranging from measurements nested within participants, to counties nested within states or students nested within classrooms often find themselves in need of modeling tools to reflect this structure of their data. In R there are two predominant ways to fit multilevel models that account for such structure in the data. These tutorials will show the user how to use both the lme4 package in R to fit linear and nonlinear mixed effect models, and to use rstan to fit fully Bayesian multilevel models. The focus here will be on how to fit the models in R and not the theory behind the models. For background on multilevel modeling, see the references. [1]
Jared walks the reader through adding the required packages, obtaining sample data and performing analysis on the sample data.
If you think about it, all data points are “nested” in one complex hierarchical structure or another.
Sometimes we choose to ignore those structures and sometimes we account for some chosen subset of complex hierarchical structures.
The important point being that our models may be useful but they are not the subjects being modeled.